U.S. patent application number 11/365342 was filed with the patent office on 2006-11-23 for method and software for analyzing voice data of a telephonic communication and generating a retention strategy therefrom.
Invention is credited to Douglas Brown, Keene Hedges Capers, Kelly Conway, Christopher Danson, David Gustafson.
Application Number | 20060265089 11/365342 |
Document ID | / |
Family ID | 38459360 |
Filed Date | 2006-11-23 |
United States Patent
Application |
20060265089 |
Kind Code |
A1 |
Conway; Kelly ; et
al. |
November 23, 2006 |
Method and software for analyzing voice data of a telephonic
communication and generating a retention strategy therefrom
Abstract
A computer program for analyzing a telephone call between a
customer and a call center is provided. The computer program
comprises a code segment for analyzing a telephonic communication
by applying a pre-determined retention attrition criteria to the
telephonic communication to calculate an attrition probability, a
code segment for receiving customer value data associated with the
customer, a code segment for comparing the attrition probability
with the customer value data, and a code segment for generating a
retention strategy based on comparing the attrition probability
with the customer value data.
Inventors: |
Conway; Kelly; (Lake Bluff,
IL) ; Gustafson; David; (Lake Forest, IL) ;
Danson; Christopher; (Austin, TX) ; Capers; Keene
Hedges; (La Jolla, CA) ; Brown; Douglas;
(Austin, TX) |
Correspondence
Address: |
BRENT A. HAWKINS, ESQ.;WALLENSTEIN WAGNER & ROCKEY, LTD.
311 South Wacker Drive - 5300
Chicago
IL
60606
US
|
Family ID: |
38459360 |
Appl. No.: |
11/365342 |
Filed: |
March 1, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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11131844 |
May 18, 2005 |
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11365342 |
Mar 1, 2006 |
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Current U.S.
Class: |
700/94 |
Current CPC
Class: |
G10L 17/26 20130101;
H04M 3/5175 20130101; G10L 2015/227 20130101; G10L 2015/088
20130101; G10L 15/22 20130101; H04M 7/0084 20130101; H04M 3/42221
20130101; H04M 7/0057 20130101; H04M 3/08 20130101 |
Class at
Publication: |
700/094 |
International
Class: |
G06F 17/00 20060101
G06F017/00 |
Claims
1. A computer program for analyzing a telephone call between a
customer and a call center comprising: a code segment for analyzing
voice data of a customer by mining the voice data and applying a
pre-determined linguist model to the voice data to calculate an
attrition probability; a code segment for receiving customer value
data associated with the customer; a code segment for comparing the
attrition probability with the customer value data; and, a code
segment for generating a retention strategy based on comparing the
attrition probability with the customer value data.
2. The computer program of claim 1 further comprising a code
segment for separating a telephonic communication into at least a
first constituent voice data and a second constituent voice data
wherein in the code segment for analyzing voice data of a customer
by mining the voice data, the first constituent voice data is
analyzed.
3. The computer program of claim 1 further comprising a code
segment for generating a notification.
4. The computer program of claim I further comprising a code
segment for automatically generating a responsive communication
based on the retention strategy wherein the responsive
communication is at least one of an email, a voice communication,
and a written communication.
5. The computer program of claim 4 wherein the type responsive
communication generated is based on at least one of behavioral
assessment data, distress assessment data and phone event data.
6. The computer program of claim 1, further comprising a code
segment for generating event data corresponding to at least one
identifying indicia and time interval, the event data comprising at
least one of behavioral assessment data, distress assessment data
and phone event data.
7. The computer program of claim 6 further comprising a code
segment for analyzing event data and a code segment for generating
the retention strategy based on the analysis of the event data.
8. The computer program of claim 1, further comprising a code
segment for generating an attrition probability score based on the
attrition probability, wherein in the code segment for generating
the retention strategy, the attrition probability score is compared
with the customer value data.
9. The method of claim 1 wherein the pre-determined linguist model
is at least a pre-determined linguist-based psychological
behavioral model.
10. A computer program for analyzing a telephone call between a
customer and a call center comprising: a code segment for analyzing
a telephonic communication by applying a pre-determined retention
attrition criteria to the telephonic communication to calculate an
attrition probability; a code segment for receiving customer value
data associated with the customer; a code segment for comparing the
attrition probability with the customer value data; and, a code
segment for generating a retention strategy based on comparing the
attrition probability with the customer value data.
11. The computer program of claim 10 further comprising a code
segment for separating a telephonic communication into at least a
first constituent voice data and a second constituent voice data
wherein in the code segment for analyzing the telephonic
communication, at least one of the first constituent voice data and
the second constituent voice data is analyzed by mining the
respective voice data and applying a pre-determined linguist model
to the voice data to calculate the attrition probability.
12. The computer program of claim 10 further comprising a code
segment for generating a notification.
13. The computer program of claim 10 further comprising a code
segment for automatically generating a responsive communication
based on the retention strategy wherein the responsive
communication is at least one of an email, a voice communication,
and a written communication.
14. The computer program of claim 13 wherein the type responsive
communication generated is based on at least one of behavioral
assessment data, distress assessment data and phone event data.
15. The computer program of claim 10, further comprising a code
segment for generating event data corresponding to at least one
identifying indicia and time interval, the event data comprising at
least one of behavioral assessment data, distress assessment data
and phone event data.
16. The computer program of claim 15 wherein in the code segment
for generating the retention strategy, the retention strategy is
generated by at least analyzing the event data.
17. The computer program of claim 10, further comprising a code
segment for generating an attrition probability score based on the
attrition probability, wherein in the code segment for generating
the retention strategy, the attrition probability score is compared
with the customer value data.
18. The computer program of claim 10 wherein the pre-determined
linguist model is at least a pre-determined linguist-based
psychological behavioral model.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This is a continuation-in-part application of co-pending
U.S. patent application No. 11/131,844 entitled "A Method And
System For Recording An Electronic Communication And Extracting
Constituent Audio Data Therefrom," filed May 18, 2005, the
disclosure of which is incorporated herein by reference. The
present invention also relates to an previously filed U.S. patent
application No. 11/131,850 entitled "A Method And System For
Analyzing Separated Voice Data Of A Telephonic Communication
Between A Customer And Contact Center By Applying A Psychological
Behavioral Model Thereto," filed May 18, 2005, the disclosure of
which is incorporated herein by reference; previously filed U.S.
patent application No. 11/131,843 entitled "Graphical User
Interface For Interactive Display Of Data Resulting From
Application Of A Psychological Behavioral Model To A Telephonic
Communication Between A Customer And A Contact Center," filed May
18, 200, the disclosure of which is incorporated herein by
reference; and, previously filed U.S. patent application No.
11/131,846 entitled "A Method And System For Analyzing Separated
Voice Data Of A Telephonic Communication Between A Customer And A
Contact Center By Applying A Psychological Behavioral Model
Thereto," filed May 18, 2005, the disclosure of which is
incorporated herein by reference.
TECHNICAL FIELD
[0002] The invention relates to a method and system for analyzing
an electronic communication, more particularly, to analyzing a
telephone communication between a customer and a contact center by
applying a psychological behavioral model thereto.
BACKGROUND OF THE INVENTION
[0003] It is known to utilize telephone call centers to facilitate
the receipt, response and routing of incoming telephone calls
relating to customer service, retention, and sales. Generally, a
customer is in contact with a customer service representative
("CSR") or call center agent who is responsible for answering the
customer's inquiries and/or directing the customer to the
appropriate individual, department, information source, or service
as required to satisfy the customer's needs.
[0004] It is also well known to monitor calls between a customer
and a call center agent. Accordingly, call centers typically employ
individuals responsible for listening to the conversation between
the customer and the agent. Many companies have in-house call
centers to respond to customers complaints and inquiries. In many
case, however, it has been found to be cost effective for a company
to hire third party telephone call centers to handle such
inquiries. As such, the call centers may be located thousands of
miles away from the actual sought manufacturer or individual. This
often results in use of inconsistent and subjective methods of
monitoring, training and evaluating call center agents. These
methods also may vary widely from call center to call center.
[0005] While monitoring such calls may occur in real time, it is
often more efficient and useful to record the call for later
review. Information gathered from the calls is typically used to
monitor the performance of the call center agents to identify
possible training needs. Based on the review and analysis of the
conversation, a monitor will make suggestions or recommendations to
improve the quality of the customer interaction.
[0006] Accordingly, there is a need in customer relationship
management ("CRM") for an objective tool useful in improving the
quality of customer interactions with agents and ultimately
customer relationships. In particular, a need exists for an
objective monitoring and analysis tool which provides information
about a customer's perception of an interaction during a call. In
the past, post-call data collection methods have been used to
survey callers for feedback. This feedback may be subsequently used
by a supervisor or trainer to evaluate an agent. Although such
surveys have enjoyed some degree of success, their usefulness is
directly tied to a customer's willingness to provide post-call
data.
[0007] More "passive" methods have also been employed to collect
data relating to a customer's in-call experience. For example, U.S.
Pat. No. 6,724,887 to Eilbacher et al. is directed to a method and
system for analyzing a customer communication with a contact
center. According to Eilbacher, a contact center may include a
monitoring system which records customer communications and a
customer experience analyzing unit which reviews the customer
communications. The customer experience analyzing unit identifies
at least one parameter of the customer communications and
automatically determines whether the identified parameter of the
customer communications indicates a negative or unsatisfactory
experience. According to Eilbacher, a stress analysis may be
performed on audio telephone calls to determine a stress parameter
by processing the audio portions of the telephone calls. From this,
it can then be determined whether the customer experience of the
caller was satisfactory or unsatisfactory.
[0008] While the method of Eilbacher provides some benefit with
respect to reaching an ultimate conclusion as to whether a
customer's experience was satisfactory or unsatisfactory, the
method provides little insight into the reasons for an experiential
outcome. As such, the method of Eilbacher provides only limited
value in training agents for future customer communications.
Accordingly, there exists a need for a system that analyzes the
underlying behavioral characteristics of a customer and agent so
that data relating to these behavioral characteristics can be used
for subsequent analysis and training.
[0009] Systems such as stress analysis systems, spectral analysis
models and word-spotting models also exist for determining certain
characteristics of audible sounds associated with a communication.
For example, systems such as those disclosed in U.S. Pat. No.
6,480,826 to Pertrushin provide a system and method for determining
emotions in a voice signal. However, like Eilbacher, these systems
also provide only limited value in training customer service agents
for future customer interactions. Moreover, such methods have
limited statistical accuracy in determining stimuli for events
occurring throughout an interaction.
[0010] It is well known that certain psychological behavioral
models have been developed as tools to evaluate and understand how
and/or why one person or a group of people interacts with another
person or group of people. The Process Communication Model.RTM.
("PCM") developed by Dr. Taibi Kahler is an example of one such
behavioral model. Specifically, PCM presupposes that all people
fall primarily into one of six basic personality types: Reactor,
Workaholic, Persister, Dreamer, Rebel and Promoter. Although each
person is one of these six types, all people have parts of all six
types within them arranged like a "six-tier configuration." Each of
the six types learns differently, is motivated differently,
communicates differently, and has a different sequence of negative
behaviors in which they engage when they are in distress.
Importantly each PCM personality type responds positively or
negatively to communications that include tones or messages
commonly associated with another of the PCM personality types.
Thus, an understanding of a communicant's PCM personality type
offers guidance as to an appropriate responsive tone or message.
There exists a need for a system and method that analyzes the
underlying behavioral characteristics of a customer and agent
communication by automatically applying a psychological behavioral
model such as, for example PCM, to the communication.
[0011] Devices and software for recording and logging calls to a
call center are well known. However, application of word-spotting
analytical tools to recorded audio communications can pose
problems. Devices and software that convert recorded or unrecorded
audio signals to text files are also known the art. But,
translation of audio signals to text files often results in lost
voice data due to necessary conditioning and/or compression of the
audio signal. Accordingly, a need also exists to provide a system
that allows a contact center to capture audio signals and telephony
events with sufficient clarity to accurately apply a
linguistic-based psychological behavioral analytic tool to a
telephonic communication.
[0012] The present invention is provided to solve the problems
discussed above and other problems, and to provide advantages and
aspects not previously provided. A full discussion of the features
and advantages of the present invention is deferred to the
following detailed description, which proceeds with reference to
the accompanying drawings.
SUMMARY OF THE INVENTION
[0013] According to the present invention, a method for analyzing a
telephonic communication between a customer and a contact center is
provided. According to the method, a telephonic communication is
separated into at least first constituent voice data and second
constituent voice data. One of the first and second constituent
voice data is analyzed by mining the voice data and applying a
predetermined linguistic-based psychological behavioral model to
one of the separated first and second constituent voice data.
Behavioral assessment data is generated which corresponds to the
analyzed voice data.
[0014] According to another aspect of the present invention, the
telephonic communication is received in digital format. The step of
separating the communication into at least a first and second
constituent voice data comprises the steps of identifying a
communication protocol associated with the telephonic
communication, and recording the telephonic communication to a
first electronic data file. The first electronic data file is
comprised of a first and second audio track. The first constituent
voice data is automatically recorded on the first audio track based
on the identified communication protocol, and the second
constituent voice data is automatically recorded on the second
audio track based on the identified communication protocol. At
least one of the first and second constituent voice data recorded
on the corresponding first and second track is separated from the
first electronic data file. It is also contemplated that two first
data files can be created, wherein the first audio track is
recorded to one of the first data file and the second audio track
is recorded to the other first data file.
[0015] According to another aspect of the present invention, the
method described above further comprises the step of generating a
text file before the analyzing step. The text file includes a
textual translation of either or both of the first and second
constituent voice data. The analysis is then performed on the
translated constituent voice data in the text file.
[0016] According to another aspect of the present invention, the
predetermined linguistic-based psychological behavioral model is
adapted to assess distress levels in a communication. Accordingly,
the method further comprises the step of generating distress
assessment data corresponding to the analyzed second constituent
voice data.
[0017] According to yet another aspect of the present invention
event data is generated. The event data corresponds to at least one
identifying indicia and time interval. The event data includes at
least one of behavioral assessment data or distress assessment
data. It is also contemplated that both behavioral assessment data
and distress assessment data are included in the event data.
[0018] According to still another aspect of the present invention,
the telephonic communication is one of a plurality of telephonic
communications. Accordingly, the method further comprises the step
of categorizing the telephonic communication as one of a plurality
of call types and/or customer categories. The telephonic
communication to be analyzed is selected from the plurality of
telephonic communications based upon the call type and/or the
customer category in which the telephonic communication is
categorized.
[0019] According to still another aspect of the present invention,
a responsive communication to the telephonic communication is
automatically generated based on the event data generated as result
of the analysis.
[0020] According to another aspect of the present invention, a
computer program for analyzing a telephonic communication is
provided. The computer program is embodied on a computer readable
storage medium adapted to control a computer. The computer program
comprises a plurality of code segments for performing the analysis
of the telephonic communication. In particular, a code segment
separates a telephonic communication into first constituent voice
data and second constituent voice data. The computer program also
has a code segment that analyzes one of the first and second voice
data by applying a predetermined psychological behavioral model to
one of the separated first and second constituent voice data. And,
a code segment is provided for generating behavioral assessment
data corresponding to the analyzed constituent voice data.
[0021] According to yet another aspect of the present invention,
the computer program comprises a code segment for receiving a
telephonic communication in digital format. The telephonic
communication is comprised of a first constituent voice data and a
second constituent voice data. A code segment identifies a
communication protocol associated with the telephonic
communication. A code segment is provided for separating the first
and second constituent voice data one from the other by recording
the telephonic communication in stereo format to a first electronic
data file. The first electronic data file includes a first and
second audio track. The first constituent voice data is
automatically recorded on the first audio track based on the
identified communication protocol, and the second constituent voice
data is automatically recorded on the second audio track based on
the identified communication protocol.
[0022] A code segment applies a non-linguistic based analytic tool
to the separated first constituent voice data and generates phone
event data corresponding to the analyzed first constituent voice
data. A code segment is provided for translating the first
constituent voice data into text format and storing the translated
first voice data in a first text file. A code segment analyzes the
first text file by mining the text file and applying a
predetermined linguistic-based psychological behavioral model to
the text file. Either or both of behavioral assessment data and
distress assessment data corresponding to the analyzed first voice
data is generated therefrom.
[0023] According to another aspect of the present invention, the
above analysis is performed on the second constituent voice data.
Additionally, a code segment is provided for generating call
assessment data by comparatively analyzing the behavioral
assessment data and distress assessment data corresponding to the
analyzed first voice data and the behavioral assessment data and
distress assessment data corresponding to the analyzed second voice
data. The computer program has a code segment for outputting event
data which is comprised of call assessment data corresponding to at
least one identifying indicia and at least one predetermined time
interval.
[0024] According to still another aspect of the present invention,
a method for analyzing an electronic communication is provided. The
method comprises the step of receiving an electronic communication
in digital format. The electronic communication includes
communication data. The communication data is analyzed by applying
a predetermined linguistic-based psychological behavioral model
thereto. Behavioral assessment data corresponding to the analyzed
communication data is generated therefrom.
[0025] The method described can be embodied in a computer program
stored on a computer readable media. The a computer program would
include code segments or routines to enable all of the functional
aspects of the interface described or shown herein
[0026] According to another aspect of the invention, a computer
program for training a customer service representative by analyzing
a telephonic communication between a customer and a contact center
is provided. A code segment selects at least one identifying
criteria. A code segment identifies a pre-recorded first telephonic
communication corresponding to the selected identifying criteria.
The first telephonic communication has first event data associated
therewith. A code segment generates coaching assessment data
corresponding to the identified pre-recorded first telephonic
communication. A code segment identifies a pre-recorded second
telephonic communication corresponding to the selected identifying
criteria. The second telephonic communication has second event data
associated therewith. A code segment compares the identified
pre-recorded second telephonic communication to the identified
first telephonic communication within the coaching assessment data.
A code segment generates a notification based on the comparison of
the identified pre-recorded second telephonic communication with
the identified first telephonic communication within the coaching
assessment data.
[0027] According to yet another aspect of the present invention, a
code segment generates a first performance score for the coaching
assessment. A code segment generates a second performance score for
the pre-recorded second telephonic communication. The notification
is generated based on a comparison of first performance score with
the second performance score.
[0028] According to still another aspect of the present invention,
a code segment identifies a plurality of pre-recorded first
telephonic communications based on at least one identifying
criteria. Each of the first telephonic communications has first
event data associated therewith. A code segment for identifies a
plurality of pre-recorded second telephonic communications based on
at least one identifying criteria. Each of the second telephonic
communications having second event data associated therewith. A
code segment generates a first performance score for each of the
plurality of prerecorded first telephonic communications and a code
segment for generates a second performance score for each of the
plurality of prerecorded second telephonic conmmunications. A code
segment generates a notification if a predetermined number of
second performance scores are at least one of less than a
predetermined threshold of the first performance scores and greater
than a predetermined threshold of the first performance scores.
[0029] According to another aspect of the present invention, a
computer program for training a customer service representative by
analyzing a telephonic communication between a customer and a
contact center is provided, A code segment selects at least one
identifying criteria. A code segment identifies a pre-recorded
first telephonic communications corresponding to the selected
identifying criteria. The first telephonic communication having
first event data associated therewith. A code segment generates
coaching assessment data corresponding to the identified
pre-recorded first telephonic communication. A code segment
compares the identified first telephonic communication within the
coaching assessment data with a predetermined identifying criteria
value threshold. A code segment generates a notification based on
the comparison of the identified first telephonic communication
with the coaching assessment data with a predetermined identifying
criteria value threshold.
[0030] According to yet another aspect of the invention, a code
segment generates a first performance score for the coaching
assessment data. A code segment generates a second performance for
the identifying criteria value threshold. A code segment for
generates a notification. The notification is generated based on a
comparison of first performance score and the second performance
score.
[0031] According to another aspect of the invention, a code segment
identifies a plurality of pre-recorded first telephonic
communications based on at least one identifying criteria. Each of
the first telephonic communications having first event data
associated therewith. A code segment generates a first performance
score for each of the plurality of prerecorded first telephonic
communications based on the at least one identifying criteria. A
code segment generates a second performance score based on the
identifying criteria value threshold. A code segment generates a
notification. The notification is generated if a predetermined
threshold of first performance scores are at least one of less than
the second performance score and greater than the second
performance scores.
[0032] According to another aspect of the present invention a
computer program for analyzing a telephone call between a customer
and a call center is provided. A code segment analyzes a telephonic
communication by applying a pre-determined retention attrition
criteria to the telephonic communication to calculate an attrition
probability. A code segment receives customer value data associated
with the customer and a code segment compares the attrition
probability with the customer value data. A code segment generates
a retention strategy based on comparing the attrition probability
with the customer value data. The retention strategy can be
generated based on event data, such as behavioral assessment data,
distress assessment data and phone event data.
[0033] According to another aspect of the present invention, the
computer program comprises a code segment for separating a
telephonic communication into at least a first constituent voice
data and a second constituent voice data wherein in the code
segment for analyzing the telephonic communication. At least one of
the first constituent voice data and the second constituent voice
data is analyzed by mining the respective voice data and applying a
pre-determined linguist model to the voice data to calculate the
attrition probability.
[0034] According to yet another aspect of the present invention,
the computer program comprises a code segment for generating a
notification. The notification can be a responsive communication
generated based on the retention strategy wherein the responsive
communication is at least one of an email, a voice communication,
and a written communication.
[0035] According to another aspect of the invention, the computer
program comprises a code segment for generating an attrition
probability score based on the attrition probability, wherein in
the code segment for generating the retention strategy, the
attrition probability score is compared with the customer value
data.
[0036] According to still another aspect of the present invention,
the computer program further comprises a code segment for
generating a graphical user interface ("GUI"). The GUI is adapted
to display a first field for enabling identification of customer
interaction event information on a display. The customer
interaction event information includes call assessment data based
on the psychological behavioral model applied to the analyzed
constituent voice data of each customer interaction event. The
computer program also includes a code segment for receiving input
from a user for identifying at least a first customer interaction
event. A code segment is also provided for displaying the customer
interaction event information for the first customer interaction
event.
[0037] According to one aspect of the present invention, the GUI
enables a user of the system to locate one or more caller
interaction events (i.e., calls between a caller and the call
center), and to display information relating to the event. In
particular, the graphical user interface provides a visual field
showing the results of the psychological behavioral model that was
applied to a separated voice data from the caller interaction
event. Moreover, the interface can include a link to an audio file
of a selected caller interaction event, and a visual representation
that tracks the portion of the caller interaction that is currently
heard as the audio file is being played.
[0038] According to one aspect of the invention, the graphical user
interface is incorporated in a system for identifying one or more
caller interaction events and displaying a psychological behavioral
model applied to a separated voice data of a customer interaction
event. The system comprises a computer coupled to a display and to
a database of caller interaction event information. The caller
interaction event information includes data resulting from
application of a psychological behavioral model to a first voice
data separated from an audio wave form of a caller interaction
event. Additionally, the caller event information can also include
additional information concerning each call, such as statistical
data relating to the caller interaction event (e.g., time, date and
length of call, caller identification, agent identification, hold
times, transfers, etc.), and a recording of the caller interaction
event.
[0039] The system also includes a processor, either at the user's
computer or at another computer, such as a central server available
over a network connection, for generating a graphical user
interface on the display. The graphical user interface comprises a
selection visual field for enabling user input of caller
interaction event parameters for selection of at least a first
caller interaction event and/or a plurality of caller interaction
events. The caller interaction event parameters can include one or
more caller interaction event identifying characteristic. These
characteristics can include, for example, the caller's name or
other identification information, a date range, the agent's name,
the call center identification, a supervisor identifier, etc. For
example, the graphical user interface can enable a user to select
all caller interaction events for a particular caller; or all calls
handled by a particular agent. Both examples can be narrowed to
cover a specified time period or interval. The interface will
display a selected caller interaction event field which provides
identification of caller interaction events corresponding to the
user input of caller interaction event parameters..
[0040] The graphical user interface also includes a conversation
visual field for displaying a time-based representation of
characteristics of the caller interaction event(s) based on the
psychological behavioral model. These characteristics were
generated by the application of a psychological behavioral model to
a first voice data separated from an audio wave form of a caller
interaction event which is stored as part of the caller interaction
event information.
[0041] The conversation visual field can include a visual link to
an audio file of the caller interaction event(s). Additionally, it
may also include a graphical representation of the progress of the
first caller interaction event that corresponds to a portion of the
audio file being played. For example, the interface may show a line
representing the call and a moving pointer marking the position on
the line corresponding to the portion of the event being played.
Additionally, the time-based representation of characteristics of
the caller interaction event can include graphical or visual
characteristic elements which are also displayed in the
conversation visual field. Moreover, the characteristic elements
are located, or have pointers to, specific locations of the
graphical representation of the progress of the event corresponding
to where the element was generated by the analysis.
[0042] The graphical user interface further includes a call
statistics visual field selectable by a user for displaying
statistics pertaining to the caller interaction events. The
statistics in the call statistics visual field can include, for
example: call duration, caller talk time, agent talk time, a caller
satisfaction score, an indication of the number of silences greater
than a predetermined time period, and an agent satisfaction
score.
[0043] The graphical user interface can also include a number of
other visual fields. For example, the graphical user interface can
include a caller satisfaction report field for displaying one or
more caller satisfaction reports, or a user note field for enabling
a user of the system to place a note with the first caller
interaction event.
[0044] In accordance with another embodiment of the invention, a
method for identifying one or more caller interaction events and
displaying an analysis of a psychological behavioral model applied
to a separated voice data from the caller interaction event
comprises providing a graphical user interface for displaying a
first field for enabling identification of caller interaction event
information on a display, the caller interaction event information
including analysis data based on a psychological behavioral model
applied to a first separated voice data of each caller interaction
event; receiving input from a user for identifying at least a first
caller interaction event; and, displaying the caller interaction
event information for the first caller interaction event on the
display. The step of receiving input from a user can include
receiving at least one or more of a caller identifier, a call
center identifier, an agent identifier, a supervisor identifier,
and a date range.
[0045] The step of displaying the caller interaction event
information for the first caller interaction event on the display
can include displaying a time-based representation of
characteristics of the first caller interaction event based on the
psychological behavioral model. The method can also include
providing an audio file of the first caller interaction event. In
this regard, the displaying of the time-based representation of
characteristics of the first caller event based on the
psychological behavioral model can include displaying a graphical
representation of the progress of the first caller interaction
event that corresponds to a portion of the audio file being
played.
[0046] The graphical user interface can be generated by a user's
local computer, or from a remote server coupled to the user's
computer via a network connection. In this latter instance, the
method can further include creating a web page containing the
graphical user interface that is downloadable to a user's computer,
and downloading the page via the network connection.
[0047] The method can include providing other visual fields for
enabling other functions of the system. For example, the method can
include providing a field in the graphical user interface for
enabling a user to place a note with the information for the first
caller interaction event.
[0048] The graphical user interface described can be embodied in a
computer program stored on a computer readable media. The a
computer program would include code segments or routines to enable
all of the functional aspects of the interface described or shown
herein.
[0049] Other features and advantages of the invention will be
apparent from the following specification taken in conjunction with
the following drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0050] To understand the present invention, it will now be
described by way of example, with reference to the accompanying
drawings in which:
[0051] FIG. 1 is a block diagram of call center;
[0052] FIG. 2 is a block diagram of the recording engine and
behavioral analysis engine according to the present invention;
[0053] FIG. 3 is a block diagram of a computer used in connection
with the present invention;
[0054] FIG. 4 is a flow chart illustrating the process of analyzing
a telephonic communication in accordance with the present
invention;
[0055] FIG. 5 is a flow chart illustrating the process of analyzing
a telephonic communication in accordance with the present
invention;
[0056] FIG. 6 is a flow chart illustrating the process of analyzing
a telephonic communication in accordance with the present
invention;
[0057] FIG. 7 is a block diagram of a telephonic communication
system according to the present invention;
[0058] FIG. 8 is a block diagram of a telephonic communication
system according to the present invention;
[0059] FIG. 9 is a block diagram of a telephonic communication
system with a multi-port PSTN module according to the present
invention;
[0060] FIG. 10 is a flow chart illustrating the process of
recording and separating a telephonic communication in accordance
with the present invention;
[0061] FIG. 11 is a flow chart illustrating the process of
recording and separating a telephonic communication in accordance
with the present invention;
[0062] FIG. 12 is a flow chart illustrating the process of
analyzing separated constituent voice data of a telephonic
communication in accordance with the present invention;
[0063] FIG. 13 is a flow chart illustrating the process of
analyzing separated constituent voice data of a telephonic
communication in accordance with the present invention;
[0064] FIGS. 14-32 are graphical user interface screens of the
resultant output from the process of analyzing voice data of a
telephonic communication in accordance with the present
invention;
[0065] FIG. 33 is a flow chart illustrating the process the
training the call center agent by analyzing a telephonic
communication; and,
[0066] FIGS. 34-36 are graphical user interface screens of the
resultant output from the process of analyzing voice data of a
telephonic communication in accordance with the present
invention.
DETAILED DESCRIPTION
[0067] While this invention is susceptible of embodiments in many
different forms, there is shown in the drawings and will herein be
described in detail preferred embodiments of the invention with the
understanding that the present disclosure is to be considered as an
exemplification of the principles of the invention and is not
intended to limit the broad aspect of the invention to the
embodiments illustrated.
[0068] Referring to FIGS. 1-32, a method and system for analyzing
an electronic communication between a customer and a contact center
is provided. A "contact center" as used herein can include any
facility or system server suitable for receiving and recording
electronic communications from customers. Such communications can
include, for example, telephone calls, facsimile transmissions,
e-mails, web interactions, voice over IP ("VoIP") and video. It is
contemplated that these communications may be transmitted by and
through any type of telecommunication device and over any medium
suitable for carrying data. For example, the communications may be
transmitted by or through telephone lines, cable or wireless
communications. As shown in FIG. 1, The contact center 10 of the
present invention is adapted to receive and record varying
electronic communications 11 and data formats that represent an
interaction that may occur between a customer (or caller) 7 and a
contact center agent 9 during fulfillment of a customer/agent
transaction.
[0069] As shown in FIG. 2, the present method and system for
analyzing an electronic communication between a customer 7 and a
contact center 10 comprises a recording engine 2 and an behavioral
analysis engine 3. As will be described in further detail, an audio
communication signal is recorded, separated into constituent audio
data, and analyzed in accordance with the methods described below.
It is contemplated that the method for analyzing an electronic
communication between a customer 7 and a contact center 10 of the
present invention can be implemented by a computer program. Now is
described in more specific terms, the computer hardware associated
with operating the computer program that may be used in connection
with the present invention.
[0070] Process descriptions or blocks in figures should be
understood as representing modules, segments, or portions of code
which include one or more executable instructions for implementing
specific logical functions or steps in the process. Alternate
implementations are included within the scope of the embodiments of
the present invention in which functions may be executed out of
order from that shown or discussed, including substantially
concurrently or in reverse order, depending on the functionality
involved, as would be understood by those having ordinary skill in
the art.
[0071] FIG. 3 is a block diagram of a computer or server 12. For
purposes of understanding the hardware as described herein, the
terms "computer" and "server" have identical meanings and are
interchangeably used. Computer 12 includes control system 14. The
control system 14 of the invention can be implemented in software
(e.g., firmware), hardware, or a combination thereof. In the
currently contemplated best mode, the control system 14 is
implemented in software, as an executable program, and is executed
by one or more special or general purpose digital computer(s), such
as a personal computer (PC; IBM-compatible, Apple-compatible, or
otherwise), personal digital assistant, workstation, minicomputer,
or mainframe computer. An example of a general purpose computer
that can implement the control system 14 of the present invention
is shown in FIG. 3. The control system 14 may reside in, or have
portions residing in, any computer such as, but not limited to, a
general purpose personal computer. Therefore, computer 12 of FIG. 3
may be representative of any computer in which the control system
14 resides or partially resides.
[0072] Generally, in terms of hardware architecture, as shown in
FIG. 3, the computer 12 includes a processor 16, memory 18, and one
or more input and/or output (I/O) devices 20 (or peripherals) that
are communicatively coupled via a local interface 22. The local
interface 22 can be, for example, but not limited to, one or more
buses or other wired or wireless connections, as is known in the
art. The local interface 22 may have additional elements, which are
omitted for simplicity, such as controllers, buffers (caches),
drivers, repeaters, and receivers, to enable communications.
Further, the local interface may include address, control, and/or
data connections to enable appropriate communications among the
other computer components.
[0073] The processor 16 is a hardware device for executing
software, particularly software stored in memory 18. The processor
16 can be any custom made or commercially available processor, a
central processing unit (CPU), an auxiliary processor among several
processors associated with the computer 12, a semiconductor based
microprocessor (in the form of a microchip or chip set), a
macroprocessor, or generally any device for executing software
instructions. Examples of suitable commercially available
microprocessors are as follows: a PA-RISC series microprocessor
from Hewlett-Packard Company, an 80.times.8 or Pentium series
microprocessor from Intel Corporation, a PowerPC microprocessor
from IBM, a Sparc microprocessor from Sun Microsystems, Inc., or a
8xxx series microprocessor from Motorola Corporation.
[0074] The memory 18 can include any one or a combination of
volatile memory elements (e.g., random access memory (RAM, such as
DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g.,
ROM, hard drive, tape, CDROM, etc.). Moreover, memory 18 may
incorporate electronic, magnetic, optical, and/or other types of
storage media. The memory 18 can have a distributed architecture
where various components are situated remote from one another, but
can be accessed by the processor 16.
[0075] The software in memory 18 may include one or more separate
programs, each of which comprises an ordered listing of executable
instructions for implementing logical functions. In the example of
FIG. 3, the software in the memory 18 includes the control system
14 in accordance with the present invention and a suitable
operating system (O/S) 24. A non-exhaustive list of examples of
suitable commercially available operating systems 24 is as follows:
(a) a Windows operating system available from Microsoft
Corporation; (b) a Netware operating system available from Novell,
Inc.; (c) a Macintosh operating system available from Apple
Computer, Inc.; (d) a UNIX operating system, which is available for
purchase from many vendors, such as the Hewlett-Packard Company,
Sun Microsystems, Inc., and AT&T Corporation; (e) a LINUX
operating system, which is freeware that is readily available on
the Internet; (f) a run time Vxworks operating system from
WindRiver Systems, Inc.; or (g) an appliance-based operating
system, such as that implemented in handheld computers or personal
digital assistants (PDAs) (e.g., PalmOS available from Palm
Computing, Inc., and Windows CE available from Microsoft
Corporation). The operating system 24 essentially controls the
execution of other computer programs, such as the control system
14, and provides scheduling, input-output control, file and data
management, memory management, and communication control and
related services.
[0076] The control system 14 may be a source program, executable
program (object code), script, or any other entity comprising a set
of instructions to be performed. When a source program, the program
needs to be translated via a compiler, assembler, interpreter, or
the like, which may or may not be included within the memory 18, so
as to operate properly in connection with the O/S 24. Furthermore,
the control system 14 can be written as (a) an object oriented
programming language, which has classes of data and methods, or (b)
a procedure programming language, which has routines, subroutines,
and/or functions, for example but not limited to, C, C++, Pascal,
Basic, Fortran, Cobol, Perl, Java, and Ada. In one embodiment, the
control system 14 is written in C++. The I/O devices 20 may include
input devices, for example but not limited to, a keyboard, mouse,
scanner, microphone, touch screens, interfaces for various medical
devices, bar code readers, stylus, laser readers, radio-frequency
device readers, etc. Furthermore, the I/O devices 20 may also
include output devices, for example but not limited to, a printer,
bar code printers, displays, etc. Finally, the I/O devices 20 may
further include devices that communicate both inputs and outputs,
for instance but not limited to, a modulator/demodulator (modem;
for accessing another device, system, or network), a radio
frequency (RF) or other transceiver, a telephonic interface, a
bridge, a router, etc.
[0077] If the computer 12 is a PC, workstation, PDA, or the like,
the software in the memory 18 may further include a basic input
output system (BIOS) (not shown in FIG. 3). The BIOS is a set of
software routines that initialize and test hardware at startup,
start the O/S 24, and support the transfer of data among the
hardware devices. The BIOS is stored in ROM so that the BIOS can be
executed when the computer 12 is activated.
[0078] When the computer 12 is in operation, the processor 16 is
configured to execute software stored within the memory 18, to
communicate data to and from the memory 18, and to generally
control operations of the computer 12 pursuant to the software. The
control system 14 and the O/S 24, in whole or in part, but
typically the latter, are read by the processor 16, perhaps
buffered within the processor 16, and then executed.
[0079] When the control system 14 is implemented in software, as is
shown in FIG. 3, it should be noted that the control system 14 can
be stored on any computer readable medium for use by or in
connection with any computer related system or method. In the
context of this document, a "computer-readable medium" can be any
means that can store, communicate, propagate, or transport the
program for use by or in connection with the instruction execution
system, apparatus, or device. The computer readable medium can be
for example, but not limited to, an electronic, magnetic, optical,
electromagnetic, infrared, or semiconductor system, apparatus,
device, or propagation medium. More specific examples (a
non-exhaustive list) of the computer-readable medium would include
the following: an electrical connection (electronic) having one or
more wires, a portable computer diskette (magnetic), a random
access memory (RAM) (electronic), a read-only memory (ROM)
(electronic), an erasable programmable read-only memory (EPROM,
EEPROM, or Flash memory) (electronic), an optical fiber (optical),
and a portable compact disc read-only memory (CDROM) (optical). The
control system 14 can be embodied in any computer-readable medium
for use by or in connection with an instruction execution system,
apparatus, or device, such as a computer-based system,
processor-containing system, or other system that can fetch the
instructions from the instruction execution system, apparatus, or
device and execute the instructions.
[0080] In another embodiment, where the control system 14 is
implemented in hardware, the control system 14 can be implemented
with any or a combination of the following technologies, which are
each well known in the art: a discrete logic circuit(s) having
logic gates for implementing logic functions upon data signals, an
application specific integrated circuit (ASIC) having appropriate
combinational logic gates, a programmable gate array(s) (PGA), a
field programmable gate array (FPGA), etc.
[0081] FIG. 4 illustrates the general flow of one embodiment of the
method of analyzing voice data according to the present invention.
As shown, an uncompressed digital stereo audio waveform of a
conversation between a customer and a call center agent is recorded
and separated into customer voice data and call center agent voice
data 26. The voice data associated with the audio waveform is then
mined and analyzed using multi-stage linguistic and non-linguistic
analytic tools 28. The analysis data is stored 30 and can be
accessed by a user 31 (e.g., CSR supervisor) through an interface
portal 32 for subsequent review 32. The digital stereo audio
waveform is compressed 34 and stored 36 in an audio file which is
held on a media server 38 for subsequent access through the
interface portal 32.
[0082] The method of the present invention is configured to
postpone audio compression until analysis of the audio data is
complete. This delay allows the system to apply the analytic tools
to a truer and clearer hi-fidelity signal. The system employed in
connection with the present invention also minimizes audio
distortion, increases fidelity, eliminates gain control and
requires no additional filtering of the signal.
[0083] As shown in FIG. 6, according to one embodiment, the method
of the present invention more specifically comprises the step of
separating a telephonic communication 2 into first constituent
voice data and second constituent voice data 40. One of the first
or second constituent voice data is then separately analyzed by
applying a predetermined psychological behavioral model thereto 42
to generate behavioral assessment data 44. In one embodiment
discussed in detail below, linguistic-based behavioral models are
adapted to assess behavior based on behavioral signifiers within a
communications are employed. It is contemplated that one or more
psychological behavioral models may be applied to the voice data to
generate behavioral assessment data therefrom.
[0084] The telephonic communication 2 being analyzed can be one of
numerous calls stored within a contact center server 12, or
communicated to a contact center during a given time period.
Accordingly, the present method contemplates that the telephonic
communication 2 being subjected to analysis is selected from the
plurality of telephonic communications. The selection criteria for
determining which communication should be analyzed may vary. For
example, the communications coming into a contact center can be
automatically categorized into a plurality of call types using an
appropriate algorithm. For example, the system may employ a
word-spotting algorithm that categorizes communications 2 into
particular types or categories based on words used in the
communication. In one embodiment, each communication 2 is
automatically categorized as a service call type (e.g., a caller
requesting assistance for servicing a previously purchased
product), a retention call type (e.g., a caller expressing
indignation, or having a significant life change event), or a sales
call type (e.g., a caller purchasing an item offered by a seller).
In one scenario, it may be desirable to analyze all of the "sales
call type" communications received by a contact center during a
predetermined time frame. In that case, the user would analyze each
of the sales call type communications from that time period by
applying the predetermined psychological behavioral model to each
such communication.
[0085] Alternatively, the communications 2 may be grouped according
to customer categories, and the user may desire to analyze the
communications 2 between the call center and communicants within a
particular customer category. For example, it may be desirable for
a user to perform an analysis only of a "platinum customers"
category, consisting of high end investors, or a "high volume
distributors" category comprised of a user's best distributors.
[0086] In one embodiment the telephonic communication 2 is
telephone call in which a telephonic signal is transmitted. As many
be seen in FIGS. 7 and 8, a customer sending a telephonic signal
may access a contact center 10 through the public switched
telephone network (PSTN) 203 and an automatic call distribution
system (PBX/ACD) 205 directs the communication to one of a
plurality of agent work stations 211, 213. Each agent work station
211, 213 includes, for example, a computer 215 and a telephone
213.
[0087] When analyzing voice data, it is preferable to work from a
true and clear hi-fidelity signal. This is true both in instances
in which the voice data is being translated into a text format for
analysis using a linguistic-based psychological behavioral model
thereto, or in instance in which a linguistic-based psychological
behavioral model is being applied directly to an audio waveform,
audio stream or file containing voice data.
[0088] FIG. 7 illustrates a telephonic communication system 201,
such as a distributed private branch exchange (PBX), having a
public switched telephone network (PSTN) 203 connected to the PBX
through a PBX switch 205.
[0089] The PBX switch 205 provides an interface between the PSTN
203 and a local network. Preferably, the interface is controlled by
software stored on a telephony server 207 coupled to the PBX switch
205. The PBX switch 205, using interface software, connects trunk
and line station interfaces of the public switch telephone network
203 to stations of a local network or other peripheral devices
contemplated by one skilled in the art. Further, in another
embodiment, the PBX switch may be integrated within telephony
server 207. The stations may include various types of communication
devices connected to the network, including the telephony server
207, a recording server 209, telephone stations 21 1, and client
personal computers 213 equipped with telephone stations 215. The
local network may further include fax machines and modems.
[0090] Generally, in terms of hardware architecture, the telephony
server 207 includes a processor, memory, and one or more input
and/or output (I/O) devices (or peripherals) that are
communicatively coupled via a local interface. The processor can be
any custom-made or commercially available processor, a central
processing unit (CPU), an auxiliary processor among several
processors associated with the telephony server 207, a
semiconductor based microprocessor (in the form of a microchip or
chip set), a macroprocessor, or generally any device for executing
software instructions. The memory of the telephony server 207 can
include any one or a combination of volatile memory elements (e.g.,
random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and
nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM,
etc.). The telephony server 207 may further include a keyboard and
a mouse for control purposes, and an attached graphic monitor for
observation of software operation.
[0091] The telephony server 207 incorporates PBX control software
to control the initiation and termination of connections between
stations and via outside trunk connections to the PSTN 203. In
addition, the software may monitor the status of all telephone
stations 211 in real-time on the network and may be capable of
responding to telephony events to provide traditional telephone
service. This may include the control and generation of the
conventional signaling tones such as dial tones, busy tones, ring
back tones, as well as the connection and termination of media
streams between telephones on the local network. Further, the PBX
control software may use a multi-port module 223 and PCs to
implement standard PBX functions such as the initiation and
termination of telephone calls, either across the network or to
outside trunk lines, the ability to put calls on hold, to transfer,
park and pick up calls, to conference multiple callers, and to
provide caller ID information. Telephony applications such as voice
mail and auto attendant may be implemented by application software
using the PBX as a network telephony services provider.
[0092] Referring to FIG. 9, in one embodiment, the telephony server
207 is equipped with multi-port PSTN module 223 having circuitry
and software to implement a trunk interface 217 and a local network
interface 219. The PSTN module 223 comprises a control processor
221 to manage the transmission and reception of network messages
between the PBX switch 205 and the telephony network server 207.
The control processor 221 is also capable of directing network
messages between the PBX switch 205, the local network interface
291, the telephony network server 207, and the trunk interface 217.
In the one embodiment, the local network uses Transmission Control
ProtocoVInternet Protocol (TCP/IP). The network messages may
contain computer data, telephony transmission supervision,
signaling and various media streams, such as audio data and video
data. The control processor 221 directs network messages containing
computer data from the PBX switch 205 to the telephony network
server 207 directly through the multi-port PSTN module 223.
[0093] The control processor 221 may include buffer storage and
control logic to convert media streams from one format to another,
if necessary, between the trunk interface 217 and local network.
The trunk interface 217 provides interconnection with the trunk
circuits of the PSTN 203. The local network interface 219 provides
conventional software and circuitry to enable the telephony server
207 to access the local network. The buffer RAM and control logic
implement efficient transfer of media streams between the trunk
interface 217, the telephony server 207, the digital signal
processor 225, and the local network interface 219.
[0094] The trunk interface 217 utilizes conventional telephony
trunk transmission supervision and signaling protocols required to
interface with the outside trunk circuits from the PSTN 203. The
trunk lines carry various types of telephony signals such as
transmission supervision and signaling, audio, fax, or modem data
to provide plain old telephone service (POTS). In addition, the
trunk lines may carry other communication formats such T1, ISDN or
fiber service to provide telephony or multimedia data images,
video, text or audio.
[0095] The control processor 221 manages real-time telephony event
handling pertaining to the telephone trunk line interfaces,
including managing the efficient use of digital signal processor
resources for the detection of caller ID, DTMF, call progress and
other conventional forms of signaling found on trunk lines. The
control processor 221 also manages the generation of telephony
tones for dialing and other purposes, and controls the connection
state, impedance matching, and echo cancellation of individual
trunk line interfaces on the multi-port PSTN module 223.
[0096] Preferably, conventional PBX signaling is utilized between
trunk and station, or station and station, such that data is
translated into network messages that convey information relating
to real-time telephony events on the network, or instructions to
the network adapters of the stations to generate the appropriate
signals and behavior to support normal voice communication, or
instructions to connect voice media streams using standard
connections and signaling protocols. Network messages are sent from
the control processor 221 to the telephony server 207 to notify the
PBX software in the telephony server 207 of real-time telephony
events on the attached trunk lines. Network messages are received
from the PBX Switch 205 to implement telephone call supervision and
may control the set-up and elimination of media streams for voice
transmission.
[0097] The local network interface 219 includes conventional
circuitry to interface with the local network. The specific
circuitry is dependent on the signal protocol utilized in the local
network. In one embodiment, the local network may be a local area
network (LAN) utilizing IP telephony. IP telephony integrates audio
and video stream control with legacy telephony functions and may be
supported through the H.323 protocol. H.323 is an International
Telecommunication Union-Telecommunications protocol used to provide
voice and video services over data networks. H.323 permits users to
make point-to-point audio and video phone calls over a local area
network. IP telephony systems can be integrated with the public
telephone system through a local network interface 219, such as an
IP/PBX-PSTN gateway, thereby allowing a user to place telephone
calls from an enabled computer. For example, a call from an IP
telephony client to a conventional telephone would be routed on the
LAN to the IP/PBX-PSTN gateway. The IP/PBX-PSTN gateway translates
H.323 protocol to conventional telephone protocol and routes the
call over the conventional telephone network to its destination.
Conversely, an incoming call from the PSTN 203 is routed to the
IP/PBX-PSTN gateway and translates the conventional telephone
protocol to H.323 protocol.
[0098] As noted above, PBX trunk control messages are transmitted
from the telephony server 207 to the control processor 221 of the
multi-port PSTN. In contrast, network messages containing media
streams of digital representations of real-time voice are
transmitted between the trunk interface 217 and local network
interface 219 using the digital signal processor 225. The digital
signal processor 225 may include buffer storage and control logic.
Preferably, the buffer storage and control logic implement a
first-in-first-out (FIFO) data buffering scheme for transmitting
digital representations of voice audio between the local network to
the trunk interface 217. It is noted that the digital signal
processor 225 may be integrated with the control processor 221 on a
single microprocessor.
[0099] The digital signal processor 225 may include a coder/decoder
(CODEC) connected to the control processor 221. The CODEC may be a
type TCM29c13 integrated circuit made by Texas Instruments, Inc. In
one embodiment, the digital signal processor 225 receives an analog
or digital voice signal from a station within the network or from
the trunk lines of the PSTN 203. The CODEC converts the analog
voice signal into in a digital from, such as digital data packets.
It should be noted that the CODEC is not used when connection is
made to digital lines and devices. From the CODEC, the digital data
is transmitted to the digital signal processor 225 where telephone
functions take place. The digital data is then passed to the
control processor 221 which accumulates the data bytes from the
digital signal processor 225. It is preferred that the data bytes
are stored in a first-in-first-out (FIFO) memory buffer until there
is sufficient data for one data packet to be sent according to the
particular network protocol of the local network. The specific
number of bytes transmitted per data packet depends on network
latency requirements as selected by one of ordinary skill in the
art. Once a data packet is created, the data packet is sent to the
appropriate destination on the local network through the local
network interface 219. Among other information, the data packet
contains a source address, a destination address, and audio data.
The source address identifies the location the audio data
originated from and the destination address identifies the location
the audio data is to be sent.
[0100] The system permits bi-directional communication by
implementing a return path allowing data from the local network,
through the local network interface 219, to be sent to the PSTN 203
through the multi-line PSTN trunk interface 217. Data streams from
the local network are received by the local network interface 219
and translated from the protocol utilized on the local network to
the protocol utilized on the PSTN 203. The conversion of data may
be performed as the inverse operation of the conversion described
above relating to the IP/PBX-PSTN gateway. The data stream is
restored in appropriate form suitable for transmission through to
either a connected telephone 211, 215 or an interface trunk 217 of
the PSTN module 223, or a digital interface such as a TI line or
ISDN. In addition, digital data may be converted to analog data for
transmission over the PSTN 203.
[0101] Generally, the PBX switch of the present invention may be
implemented with hardware or virtually. A hardware PBX has
equipment located local to the user of the PBX system. The PBX
switch 205 utilized may be a standard PBX manufactured by Avaya,
Siemens AG, NEC, Nortel, Toshiba, Fujitsu, Vodavi, Mitel, Ericsson,
Panasonic, or InterTel. In contrast, a virtual PBX has equipment
located at a central telephone service provider and delivers the
PBX as a service over the PSTN 203.
[0102] As illustrated in FIG. 1, the system includes a recording
server 209 for recording and separating network messages
transmitted within the system. The recording server 209 may be
connected to a port on the local network, as seen in FIG. 1.
Alternatively, the recording server 209 may be connected to the
PSTN trunk line as illustrated in FIG. 1A. The recording server 209
includes a control system software, such as recording software. The
recording software of the invention can be implemented in software
(e.g., firmware), hardware, or a combination thereof. In the
currently contemplated best mode, the recording software is
implemented in software, as an executable program, and is executed
by one or more special or general purpose digital computer(s), such
as a personal computer (PC; IBM-compatible, Apple-compatible, or
otherwise), personal digital assistant, workstation, minicomputer,
or mainframe computer. An example of a general purpose computer
that can implement the recording software of the present invention
is shown in FIG. 3. The recording software may reside in, or have
portions residing in, any computer such as, but not limited to, a
general purpose personal computer. Therefore, recording server 209
of FIG. 3 may be representative of any type of computer in which
the recording software resides or partially resides.
[0103] Generally, hardware architecture is the same as that
discussed above and shown in FIG. 3. Specifically, the recording
server 209 includes a processor, memory, and one or more input
and/or output (I/O) devices (or peripherals) that are
communicatively coupled via a local interface as previously
described. The local interface can be, for example, but not limited
to, one or more buses or other wired or wireless connections, as is
known in the art. The local interface may have additional elements,
which are omitted for simplicity, such as controllers, buffers
(caches), drivers, repeaters, and receivers, to enable
communications. Further, the local interface may include address,
control, and/or data connections to enable appropriate
communications among the other computer components.
[0104] As noted above, the recording server 209 incorporates
recording software for recording and separating a signal based on
the source address and/or destination address of the signal. The
method utilized by the recording server 209 depends on the
communication protocol utilized on the communication lines to which
the recording server 209 is coupled. In the communication system
contemplated by the present invention, the signal carrying audio
data of a communication between at least two users may be an analog
signal or a digital signal in the form of a network message. In one
embodiment, the signal is an audio data transmitted according to a
signaling protocol, for example the H.323 protocol described
above.
[0105] An example of a communication between an outside caller and
a call center agent utilizing the present system 200 is illustrated
in FIG. 10 and described herein. In the embodiment of FIG. 10, when
an outside caller reaches the system through the multi-line
interface trunk 217, their voice signal is digitized (if needed) in
the manner described above, and converted into digital data packets
235 according to the communication protocol utilized on the local
network of the system. The data packet 235 comprises a source
address identifying the address of the outside caller, a
destination address identifying the address of the call center
agent, and first constituent audio data comprising at least a
portion of the outside callers voice. The data packet 235 can
further comprise routing data identifying how the data packet 235
should be routed through the system and other relevant data. Once
the data packet 235 is created, the data packet 235 is sent to the
appropriate destination on the local network, such as to a call
center agent, through the local network interface 219. The PBX
and/or an automatic call distributor (ACD) can determine the
initial communication setup, such as the connection state,
impedance matching, and echo cancellation, according to
predetermined criteria.
[0106] Similar to the process described above, when the call center
agent speaks, their voice is digitized (if needed) and converted
into digital data packet 235 according to the communication
protocol utilized on the local network. The data packet 235
comprises a source address identifying the address of the call
center agent, a destination address identifying the address of the
outside caller, and second constituent audio data comprising at
least a portion of the call center agent's voice. The data packet
235 is received by the local network interface 219 and translated
from the communication protocol utilized on the local network to
the communication protocol utilized on the PSTN 203. The conversion
of data can be performed as described above. The data packet 235 is
restored in appropriate form suitable for transmission through to
either a connected telephone 211, 215 or a interface trunk 217 of
the PSTN module 223, or a digital interface such as a T1 line or
ISDN. In addition, digital data can be converted to analog data for
transmission through the PSTN 203.
[0107] The recording server 209 receives either a data packet 235
comprising: the source address identifying the address of the
outside caller, a destination address identifying the address of
the call center agent, and the first constituent audio data
comprising at least a portion of the outside callers voice; or a
data packet 235 comprising a source address identifying the address
of the call center agent, a destination address identifying the
address of the outside caller, and second constituent audio data
comprising at least a portion of the customer's agent voice. It is
understood by one of ordinary skill in the art that the recording
server 209 is programmed to identify the communication protocol
utilized by the local network and extract the audio data within the
data packet 235. In one embodiment, the recording server 209 can
automatically identify the utilized communication protocol from a
plurality of communication protocols. The plurality of
communication protocols can be stored in local memory or accessed
from a remote database.
[0108] The recording server 209 comprises recording software to
record the communication session between the outside caller and the
call center agent in a single data file in a stereo format. The
first data file 241 has at least a first audio track 237 and a
second audio track 237. Once a telephone connection is established
between an outside caller and a call center agent, the recording
software creates a first data file 241 to record the communication
between the outside caller and the call center agent. It is
contemplated that the entire communication session or a portion of
the communication session can be recorded.
[0109] Upon receiving the data packet 235, the recording server 209
determines whether to record the audio data contained in the data
packet 235 in either the first audio track 237 or the second audio
track 239 of the first data file 241 as determined by the source
address, destination address, and/or the audio data contained
within the received data packet 235. Alternatively, two first data
files can be created, wherein the first audio track is recorded to
the one of the first data file and the second audio track is
recorded to the second first data file. In one embodiment, if the
data packet 235 comprises a source address identifying the address
of the outside caller, a destination address identifying the
address of the call center agent, and first constituent audio data,
the first constituent audio data is recorded on the first audio
track 237 of the first data file 241. Similarly, if the data packet
235 comprises a source address identifying the address of the call
center agent, a destination address identifying the address of the
outside caller, and second constituent audio data, the second
constituent audio data is recorded on the second audio track 239 of
the first data file 241. It should be noted the first and second
constituent audio data can be a digital or analog audio waveform or
a textual translation of the digital or analog waveform. The
recording process is repeated until the communication link between
the outside caller and call center agent is terminated.
[0110] As noted above, the recording server 209 can be connected to
the trunk lines of the PSTN 203 as seen in FIG. 8. The PSTN 203 can
utilize a different protocol and therefore, the recording server
209 is configured to identify the communication protocol utilized
by the PSTN 203, recognize the source and destination address of a
signal and extract the audio data from the PSTN 203. The recording
server 209 is programmed in a manner as known to one of ordinary
skill in the art.
[0111] As shown in FIG. 10, once the communication link is
terminated, the recording server 209 ends the recording session and
stores the single data file having the recorded communication
session in memory. After the first data file is stored in memory,
the recording server 209 can extract either or both of the first
constituent audio data from the first audio track of the first data
file or the second constituent audio data from the second audio
track of the first data file. In one embodiment, the first
constituent audio data extracted from the first audio track is
stored in a first constituent data file 243. Similarly, the second
constituent audio data extracted from the second audio track can be
stored in a second constituent data file 245. The first and second
constituent data files 243, 245 can be compressed before being
stored in memory. The extracted data can be in the form of a
digital or analog audio waveform or can be a textual translation of
the first or second constituent audio data. It is contemplated that
either or both of the first constituent data file 243 or the second
constituent data file 245 can be further analyzed or processed. For
example, among other processes and analyses, filtering techniques
can be applied to the first constituent data file and/or the second
constituent data file. Moreover, event data, such as silence
periods or over-talking, can be identified through analysis
techniques known to those skilled in the art.
[0112] Further, as illustrated in FIG. 10, the first constituent
data file 243 and second constituent data file 245 can be merged
together into a single second data file 247. The first and second
constituent data files can be merged in a stereo format where the
first constituent audio data from the first constituent data file
243 is stored on a first audio track of the second data file 247
and the second constituent audio data from the second constituent
data file 245 is stored on a second audio track of the second data
file 247. Alternatively, the first and second constituent data
files can be merged in a mono format where the first constituent
audio data from the first constituent data file 243 and the second
constituent audio data from the second constituent data file 245
are stored on a first audio track of the second data file 247.
Additionally, the first and second constituent audio data can be
merged into a document having a textual translation of the audio
data. In such a case, identifiers can be associated with each of
the merged first and second constituent audio data in order to
associate the merged first constituent audio data with the outside
caller, and associate the merged second constituent audio data with
the call center agent. The second data file 247 can be compressed
before being stored in memory.
[0113] It is known in the art that "cradle-to-grave" recording may
be used to record all information related to a particular telephone
call from the time the call enters the contact center to the later
of: the caller hanging up or the agent completing the transaction.
All of the interactions during the call are recorded, including
interaction with an IVR system, time spent on hold, data keyed
through the caller's key pad, conversations with the agent, and
screens displayed by the agent at his/her station during the
transaction.
[0114] As shown in FIGS. 11-13, once the first and second
constituent voice data are separated one from the other, each of
the first and second constituent voice data can be independently
mined and analyzed. It will be understood that "mining" as
referenced herein is to be considered part of the process of
analyzing the constituent voice data. It is also contemplated by
the present invention that the mining and behavioral analysis be
conducted on either or both of the constituent voice data.
[0115] Even with conventional audio mining technology, application
of linguistic-based psychological behavioral models directly to an
audio file can be very difficult. In particular, disparities in
dialect, phonemes, accents and inflections can impede or render
burdensome accurate identification of words. And while it is
contemplated by the present invention that mining and analysis in
accordance with the present invention can be applied directly to
voice data configured in audio format, in a preferred embodiment of
the present invention, the voice data to be mined and analyzed is
first translated into a text file. It will be understood by those
of skill that the translation of audio to text and subsequent data
mining may be accomplished by systems known in the art. For
example, the method of the present invention may employ software
such as that sold under the brand name Audio Mining SDK by
Scansoft, Inc., or any other audio mining software suitable for
such applications.
[0116] As shown in FIGS. 11-13, the separated voice data is mined
for behavioral signifiers associated with a linguistic-based
psychological behavioral model. In particular, the method of the
present invention searches for and identifies text-based keywords
(i.e., behavioral signifiers) relevant to a predetermined
psychological behavioral model.
[0117] According to a one embodiment of the present invention, the
psychological behavioral model used to analyze the voice data is
the Process Communication Model.RTM. ("PCM") developed by Dr. Taibi
Kahler. PCM is a psychological behavioral analytic tool which
presupposes that all people fall primarily into one of six basic
personality types: Reactor, Workaholic, Persister, Dreamer, Rebel
and Promoter. Although each person is one of these six types, all
people have parts of all six types within them arranged like a
six-tier configuration. Each of the six types learns differently,
is motivated differently, communicates differently, and has a
different sequence of negative behaviors they engage in when they
are in distress. Importantly, according to PCM, each personality
type of PCM responds positively or negatively to communications
that include tones or messages commonly associated with another of
the PCM personality types. Thus, an understanding of a
communicant's PCM personality type offers guidance as to an
appropriate responsive tone or message or wording.
[0118] According to the PCM Model the following behavioral
characteristics are associated with the respective personality
types: TABLE-US-00001 PROCESS COMMUNICATION MODEL (PCM) PERSONALITY
TYPE BEHAVIORAL CHARACTERISTICS Reactors compassionate, sensitive,
and warm; great "people skills" and enjoy working with groups of
people Workaholics responsible, logical, and organized Persisters
conscientious, dedicated, and observant; tend to follow the rules
and expect others to follow them Dreamers reflective, imaginative,
and calm Rebels creative, spontaneous, and playful Promoters
resourceful, adaptable, and charming
These behavioral characteristics may be categorized by words,
tones, gestures, postures and facial expressions, can be observed
objectively with significantly high interjudge reliability.
[0119] According to one embodiment shown in FIG. 13, the present
invention mines significant words within one or both of the
separated first and second constituent voice data, and applies PCM
to the identified words. For example, the following behavioral
signifiers (i.e., words) may be associated with the corresponding
behavioral type in the PCM Model: TABLE-US-00002 PROCESS
COMMUNICATION MODEL (PCM) PERSONALITY TYPE BEHAVIORAL SIGNIFIERS
Reactors Emotional Words Workaholics Thought Words Persisters
Opinion Words Dreamers Reflection Words Rebels Reaction Words
Promoters Action Words
[0120] In another embodiment, the present method mines for such
significant words within the merged second data file 247 described
above, and apply PCM to the identified words. Alternatively, the
first data file 241 can be mined for significant words.
[0121] As shown in FIG. 13, when a behavioral signifier is
identified within the voice data 62, the identified behavioral
signifier is executed against a system database which maintains all
of the data related to the psychological behavioral model 66. Based
on the behavioral signifiers identified in the analyzed voice data,
a predetermined algorithm 64 is used to decipher a linguistic
pattern that corresponds to one or more of the PCM personality
types 68. More specifically, the present method mines for
linguistic indicators (words and phrases) that reveal the
underlying personality characteristics of the speaker during
periods of distress. Non-linguistic indicators may also be
identified to augment or confirm the selection of a style for each
segment of speech. Looking at all the speech segments in
conjunction with personality information the software determines an
order of personality components for the caller by weighing a number
of factors such as timing, position, quantity and interaction
between the parties in the dialog.
[0122] In one embodiment, the behavioral assessment data 55
includes sales effectiveness data. According to such an embodiment,
the voice data is mined for linguist indicators to determine
situations in which the call center agent made a sale or failed at
an opportunity to make a sale. The failed opportunities may include
failure to make an offer for a sale, making an offer and failure in
completing the sale, or failure to make a cross-sale.
[0123] The resultant behavioral assessment data 55 is stored in a
database so that it may subsequently be used to comparatively
analyze against behavioral assessment data derived from analysis of
the other of the first and second constituent voice data 56. The
software considers the speech segment patterns of all parties in
the dialog as a whole to refine the behavioral and distress
assessment data of each party, making sure that the final distress
and behavioral results are consistent with patterns that occur in
human interaction. Alternatively, the raw behavioral assessment
data 55 derived from the analysis of the single voice data may be
used to evaluate qualities of a single communicant (e.g., the
customer or agent behavioral type, etc.). The results generated by
analyzing voice data through application of a psychological
behavioral model to one or both of the first and second constituent
voice data can be graphically illustrated as discussed in further
detail below.
[0124] It should be noted that, although one preferred embodiment
of the present invention uses PCM as a linguistic-based
psychological behavioral model, it is contemplated that any known
linguistic-based psychological behavioral model be employed without
departing from the present invention. It is also contemplated that
more than one linguistic-based psychological behavioral model be
used to analyze one or both of the first and second constituent
voice data.
[0125] In addition to the behavioral assessment of voice data, the
method of the present invention may also employ distress analysis
to voice data. As may be seen in FIG. 2, linguistic-based distress
analysis is preferably conducted on both the textual translation of
the voice data and the audio file containing voice data.
Accordingly, linguistic-based analytic tools as well as
non-linguistic analytic tools may be applied to the audio file. For
example, one of skill in the art may apply spectral analysis to the
audio file voice data while applying a word spotting analytical
tool to the text file. Linguistic-based word spotting analysis and
algorithms for identifying distress can be applied to the textual
translation of the communication. Preferably, the resultant
distress data is stored in a database for subsequent analysis of
the communication.
[0126] As shown in FIGS. 2, it is also often desirable to analyze
non-linguistic phone events occurring during the course of a
conversation such as hold times, transfers, "dead-air," overtalk,
etc. Accordingly, in one embodiment of the present invention, phone
event data resulting from analysis of these non-linguistic events
is generated. Preferably, the phone event data is generated by
analyzing non-linguistic information from both the separated
constituent voice data, or from the subsequently generated audio
file containing at least some of the remerged audio data of the
original audio waveform. It is also contemplated that the phone
event data can be generated before the audio waveform is
separated.
[0127] According to a preferred embodiment of the invention as
shown in FIG. 13, both the first and second constituent voice data
are mined and analyzed as discussed above 64, 66. The resulting
behavioral assessment data 55, phone event data 70 and distress
assessment data 72 from each of the analyzed first and second
constituent voice data are comparatively analyzed in view of the
parameters of the psychological behavioral model to provide an
assessment of a given communication. From this comparative
analysis, call assessment data relating to the totality of the call
may be generated 56.
[0128] Generally, call assessment data is comprised of behavioral
assessment data, phone event data and distress assessment data. The
resultant call assessment data may be subsequently viewed to
provide an objective assessment or rating of the quality,
satisfaction or appropriateness of the interaction between an agent
and a customer. In the instance in which the first and second
constituent voice data are comparatively analyzed, the call
assessment data may generate resultant data useful for
characterizing the success of the interaction between a customer
and an agent.
[0129] Thus, as shown in FIGS. 11 and 12, when a computer program
is employed according to one embodiment of the present invention, a
plurality of code segments are provided. The program comprises a
code segment for receiving a digital electronic signal carrying an
audio waveform 46. In accordance with the voice separation software
described above, a code segment identifies a communication protocol
associated with the telephonic signal 47. A code segment is also
provided to separate first and second constituent voice data of the
communication one from the other by recording the audio waveform in
stereo format to a first electronic data file which has a first and
second audio track 48. As discussed above, the first constituent
voice data is automatically recorded on the first audio track based
on the identified communication protocol, and the second
constituent voice data is automatically recorded on the second
audio track based on the identified communication protocol.
[0130] The software also includes a code segment for separately
applying a non-linguistic based analytic tool to each of the
separated first and second constituent voice data, and to generate
phone event data corresponding to the analyzed voice data 50. A
code segment translates each of the separated first and second
constituent voice data into text format and stores the respective
translated first and second constituent voice data in a first and
second text file 52. A code segment analyzes the first and second
text files by applying a predetermined linguistic-based
psychological behavioral model thereto 54. The code segment
generates either or both of behavioral assessment data and distress
assessment data corresponding to each of the analyzed first and
second constituent voice data 54.
[0131] A code segment is also provided for generating call
assessment data 56. The call assessment data is resultant of the
comparative analysis of the behavioral assessment data and distress
assessment data corresponding to the analyzed first voice data and
the behavioral assessment data and distress assessment data
corresponding to the analyzed second voice data. A code segment
then transmits an output of event data corresponding to at least
one identifying indicia (e.g., call type, call time, agent,
customer, etc.) 58. This event data is comprised of a call
assessment data corresponding to at least one identifying indicia
(e.g., a CSR name, a CSR center identifier, a customer, a customer
type, a call type, etc.) and at least one predetermined time
interval. Now will be described in detail the user interface for
accessing and manipulating the event data of an analysis.
[0132] In one embodiment of the present invention shown in FIG. 13,
the analysis of the constituent voice data includes the steps of:
translating constituent voice data to be analyzed into a text
format 60 and applying a predetermined linguistic-based
psychological behavioral model to the translated constituent voice
data. In applying the psychological behavioral model, the
translated voice data is mined 62. In this way at least one of a
plurality of behavioral signifiers associated with the
psychological behavioral model is automatically identified in the
translated voice data. When the behavioral signifiers are
identified, the behavioral signifiers are automatically associated
with at least one of a plurality of personality types 68 associated
with the psychological behavioral model 64, 66. By applying
appropriate algorithms behavioral assessment data corresponding to
the analyzed constituent voice data is generated 55.
[0133] The method and system of the present invention is useful in
improving the quality of customer interactions with agents and
ultimately customer relationships. In use, a customer wishing to
engage in a service call, a retention call or a sales will call
into (or be called by) a contact center. When the call enters the
contact center it will be routed by appropriate means to a call
center agent. As the interaction transpires, the voice data will be
recorded as described herein. Either contemporaneously with the
interaction, or after the call interaction has concluded, the
recorded voice data will be analyzed as described herein. The
results of the analysis will generate call assessment data
comprised of behavioral assessment data, distress assessment data
and phone event data. This data may be subsequently used by a
supervisor or trainer to evaluate or train an agent, or take other
remedial action such as call back the customer, etc.
[0134] As indicated above, it is often desirable to train call
center agents to improve the quality of customer interactions with
agents. Thus, as shown in FIGS. 33-36, the present invention
provides a method for training the call center agent by analyzing
telephonic communications between the call center agent and the
customer. In one embodiment, a plurality of the pre-recorded first
communications between outside callers and a specific call center
agent are identified based on an identifying criteria 601. The
pre-recorded first communication can be one of the separated
constituent voice data or the subsequently generated audio file
containing at least some of the remerged audio waveform of the
original audio waveform.
[0135] The pre-recorded first communications to be used in training
the call center agent are identified by comparatively analyzing the
identifying criteria in view of event data 602. The event data can
include behavioral assessment data, phone event data, and/or
distress assessment data of the communications. For example, the
identifying criteria can be phone event data such as excessive
hold/silence time (e.g., caller is placed on hold for greater than
predetermined time--e.g., 90 seconds--or there is a period of
silence greater than a predetermined amount time--e.g., 30 seconds)
or long duration for call type (i.e., calls that are a
predetermined percentage--e.g., 150%--over the average duration for
a given call type). Additionally, the identifying criteria can be
distress assessment data such as upset customer, unresolved issue
or program dissatisfaction or an other data associated with
distress assessment data. It is contemplated that the system
identify potential identifying criteria based on an analysis of the
behavioral assessment data, phone event data, and/or distress
assessment data of the communications.
[0136] From this comparative analysis, coaching assessment data is
generated. The coaching assessment data relates to the identified
pre-recorded first communications corresponding to the identifying
criteria 604. For example, if the identifying criteria is excessive
hold/silence time, the coaching assessment data includes
pre-recorded first communications having excessive hold/silence
time. The resulting coaching assessment data is stored in a
database so that it subsequently can be used to evaluate and/or
train the call center agent to improve performance in view of the
identifying criteria. Thus, if the identifying criteria were
excessive hold/silence time, the call center agent would be trained
to reduce the amount of excessive hold/silence time calls.
[0137] The coaching assessment data can further include first
performance data related to the overall performance of the call
center agent with respect to the identifying criteria. The first
performance data can be derived from an analysis of the identified
pre-recorded first communication with respect to all
communications--i.e., identified pre-recorded first communication
percentage (the percentage of identified pre-recorded first
communications out of total number of communications) or identified
pre-recorded communication (total number of identified pre-recorded
first communications). A first performance score for each
identified pre-recorded first communication may be generated by
analyzing each identified pre-recorded first communication and the
corresponding first performance data. A composite first performance
score may be generated corresponding to the aggregate of the first
performance scores of the plurality of identified pre-recorded
first communications.
[0138] The coaching assessment data can be comparatively analyzed
against a predetermined criteria value threshold to evaluate the
call center agent's performance or against event data derived from
a plurality of identified second pre-recorded communications to
determine if training was effective 606. As discussed above, the
threshold may be a predetermined criteria set by the call center,
the customer, or other objective or subjective criteria.
Alternatively, the threshold may set by the performance score.
[0139] In order to evaluate a call center agent, the coaching
assessment data is comparatively analyzed against a predetermined
identifying criteria value threshold. In one embodiment, the first
performance data related to the identified pre-recorded first
communication is comparatively analyzed with the predetermined
identifying criteria value threshold 614. Based on the resultant
comparative analysis, a notification is generated 616. For example,
the percentage of excessive hold/silence calls in the pre-recorded
first communications is compared with the identifying criteria
value threshold. If the percentage of excessive hold/silence calls
in the pre-recorded first communications is greater than the
identifying criteria value threshold, the call center agent is
underperforming and a notification is automatically generated
616.
[0140] In one embodiment, the coaching assessment data includes
sales effectiveness data. The sales effectiveness data related to
the identified pre-recorded first communications is comparatively
analyzed against a predetermined identifying criteria value
threshold. For example, the percentage of calls that the call
center agent failed to make an offer for a cross-sale is compared
with the identifying criteria value threshold. If the percentage of
calls that the call center agent failed to make an offer for a
cross-sale is greater than the identifying criteria value
threshold, the call center agent is underperforming, and a
notification is generated.
[0141] In another embodiment, the first performance score for each
identified pre-recorded recorded first communication is compared
with the second performance score for the identifying criteria
value threshold. In this case, if a predetermined number of first
performance scores are less than (or greater than) the identifying
criteria value threshold, a notification is generated. In another
embodiment, the composite first performance score for the
identified pre-recorded first communications is compared with the
second performance score for the identifying criteria value
threshold. If the first composite performance score is less than
(or greater than) the second composite performance score, a
notification is generated.
[0142] Preferably, the notification is an electronic communication,
such as an email transmitted to a supervisor or trainer indicating
that the call center agent is underperforming. The notification may
be any other type of communication, such as a letter, a telephone
call, or an automatically generated message on a website The
notification permits the supervisor or trainer to take remedial
action, such as set up a training session for the call center
agent. In one embodiment, the coaching assessment data related to
an identifying criteria can be comparatively analyzed against the
identifying criteria value threshold for a plurality call center
agents. Based on the collective comparative analysis, a
notification is generated if a predetermined number or percentage
of call center agents are underperforming. In this manner, the
trainer or supervisor is notified that multiple call center agents
need to be trained with respect to the same criteria.
[0143] As noted above, the identifying criteria of the coaching
assessment data can also be used to train a call center agent. In
order to determine if the call center agent training was effective,
the coaching assessment data can be comparatively analyzed against
event data derived from a plurality of identified second
pre-recorded communications. To determine if the training was
effective, the second pre-recorded communications should have taken
place after the call center agent training session. The
pre-recorded second communications are identified according to the
same identifying criteria used to identify the pre-recorded first
communications in the coaching assessment data 608. Similar to the
pre-recorded first communications, the pre-recorded second
communications can be one of the separated constituent voiced data
or the subsequently generated audio file containing at least some
of the remerged audio waveform of the original audio waveform.
[0144] Second performance data related to the overall performance
of the call center agent with respect to the pre-recorded second
communications can be generated. As with the first performance
data, the second performance data can be derived from an analysis
of the identified pre-recorded second communication with respect to
all communications--i.e., identified pre-recorded second
communication percentage (the percentage of identified pre-recorded
second communications out of total number of communications) or
identified pre-recorded communication (total number of identified
pre-recorded second communications). A second performance score for
each identified pre-recorded second communication may be generated
by analyzing each identified pre-recorded second communication and
the corresponding second performance data. A composite second
performance score may be generated corresponding to the aggregate
second performance score for each of the plurality of identified
pre-recorded second communications.
[0145] The second performance data related to the identified
pre-recorded second communications is comparatively analyzed with
the first performance data of the coaching assessment data 610.
Based on the resultant comparative analysis, a notification is
generated 612.
[0146] In one embodiment, the identified pre-recorded second
communication percentage is compared with the identified
pre-recorded first communication percentage. For example, the
percentage of excessive hold/silence calls in the pre-recorded
first communications that took place before the training session is
compared with the percentage of excessive hold/silence calls in the
pre-recorded second communications that took place after the
training session 610. If the percentage of excessive hold/silence
calls in the pre-recorded second communications is less than the
percentage of excessive hold/silence calls in the pre-recorded
first communications, the training session was successful.
Conversely, if the percentage of excessive hold/silence calls in
the pre-recorded second communications is greater than the
percentage of excessive hold/silence calls in the pre-recorded
first communications, the training session was unsuccessful and a
notification is automatically generated 612.
[0147] In another embodiment, the first performance score for each
identified pre-recorded first communication is compared with the
second performance score for each identified pre-recorded second
communication. In this case, if a predetermined number of second
performance scores are less than (or greater than) a predetermined
number of first performance scores, a notification is generated. In
another embodiment, the composite first performance score for the
identified pre-recorded first communications is compared with the
composite second performance score for the identified pre-recorded
second communications. If the second composite performance score is
less than (or greater than) the first composite performance score,
a notification is generated.
[0148] Preferably, the notification is an electronic communication,
such as an email transmitted to a supervisor or trainer indicating
that the training session for the call center agent was
unsuccessful. The notification permits the supervisor or trainer to
take remedial action, such as set up another training session for
the call center agent. In one embodiment, the coaching assessment
data related to an identifying criteria can be comparatively
analyzed against event data derived from a plurality of identified
second pre-recorded communications for a plurality of call center
agents. Based on the collective comparative analysis, a
notification is generated if a predetermined number or percentage
of call center agents have unsuccessful training sessions. In this
manner, the trainer or supervisor is notified that multiple call
center agents need to be trained with respect to the same
criteria.
[0149] As indicated above, analysis of all or portions of the call
assessment data may be used to take remedial action, such as call
back the customer, etc. This analysis and resulting responsive
communication is useful in reducing the attrition of customers who
call the contact center. Thus, as shown in FIG. 37, the present
invention the present invention provides a method for generating a
retention strategy by analyzing a telephonic communication between
a customer and a call center agent.
[0150] In one embodiment, the present method analyzes a telephonic
communication by applying a pre-determined retention attrition
analysis to the telephonic communication. Preferably, the
pre-determined retention attrition analysis mines for significant
words within one or both of the separated first and second
constituent voice data 62, and applies a linguist-based model to
identify words 650. It is contemplated that the linguist-based
model is the pre-determined linguist-based psychological behavioral
model. The linguist-based model mines for words associated with
potential attrition of the customer. In another embodiment, the
present method mines for such significant words within the merged
second data file 247 described above, and applies the
linguist-based psychological model to the identified words.
Alternatively, only the customer's voice data file can be mined for
significant words. In yet another embodiment, the predetermined
retention attrition analysis analyzes event data to generate a
retention strategy. The event data can include behavioral
assessment data, distress assessment data, and/or phone event
data.
[0151] When a behavioral signifier is identified within the voice
data 62, the identified behavioral signifier is executed against a
system database which maintains all of the data related to
attrition of a customer. Based on the behavioral signifiers
identified in the analyzed voice data, a predetermined algorithm 64
is used to calculate an attrition probability defining the
likelihood that a customer will leave the company utilizing the
call center 650. In the preferred embodiment, the attrition
probability can also include an attrition probability score that is
calculated based on the attrition probability. Looking at all the
speech segments in conjunction with attrition information the
software determines the attrition probability by weighing a number
of factors such as timing, position, quantity and interaction
between the parties in the dialog.
[0152] In another embodiment, when a particular event is identified
within the behavioral assessment data, distress assessment data
and/or phone event data, the event is executed against a system
database which maintains all of the data related to attrition of a
customer based on the particular event. Based on this
identification, an attrition probability is calculated.
[0153] The attrition probability and/or attrition probability score
is comparatively analyzed against customer value data associated
with the customer of the telephone call being analyzed 654.
Preferably, the customer value data is customer information that is
inputted or calculated by the system 652. The customer value data
may include data regarding the length of the customer relationship,
the amount of money the customer has spent during the customer
relationship, other data that assists a company in valuing a
customer or any a designation given to the customer based on the
aforementioned data. For example, the customer value data for the
customer of the telephone call may indicated that the customer is a
"platinum level" member and that the customer has been a customer
for twenty years. In the preferred embodiment, the customer value
data can also include a customer value data score that is
calculated based on the customer value data.
[0154] A customer specific retention strategy is selected from a
plurality of retention strategies stored in memory based on the
comparison of the attrition probability with the customer value
data. Alternatively, the customer specific retention strategy can
be selected based on the comparison of the attrition probability
score with the customer value data score. The retention strategies
may include, for example, a responsive written communication to the
customer, a responsive oral communication to the customer, sending
a complementary to the customer, and/or any other strategies, as
may be dictated by the customer and/or company. In one embodiment,
the responsive written communication is automatically generated as
an email or a letter addressed to the customer.
[0155] It is contemplated that a notification be generated
outlining the customer specific retention strategy. As with the
method of training a call center agent, as described above,
preferably, the notification is an electronic communication, such
as an email, transmitted to a supervisor. Alternatively, the
notification may be any other type of communication such as a
letter, a telephone call, or an automatically generated message on
a website. The notification pennits the supervisor to take remedial
action by implementing the retention strategy.
[0156] Graphical and pictorial analysis of the call assessment data
(and event data) is accessible through a portal by a subsequent
user (e.g., a supervisor, training instructor or monitor) through a
graphical user interface. A user of the system 1 described above
interact with the system 1 via a unique graphical user interface
("GUI") 400. The GUI 400 enables the user to navigate through the
system 1 to obtain desired reports and information regarding the
caller interaction events stored in memory. The GUI 400 can be part
of a software program residing in whole or in part in the a
computer 12, or it may reside in whole or in part on a server
coupled to a computer 12 via a network connection, such as through
the Internet or a local or wide area network (LAN or WAN).
Moreover, a wireless connection can be used to link to the
network.
[0157] In the embodiment shown in FIGS. 14-32, the system 1 is
accessed via an Internet connection from a computer. Known browser
technology on the computer can be implemented to reach a server
hosting the system program. The GUI 400 for the system will appear
as Internet web pages on the display of the computer.
[0158] As shown in FIG. 14, the GUI 400 initially provides the user
with a portal or "Log On" page 402 that provides fields for input
of a user name 404 and password 406 to gain access to the system.
Additionally, the GUI 400 can direct a user to one or more pages
for setting up a user name and password if one does not yet
exist.
[0159] Referring to FIG. 15, once logged into the system 1, the
user can navigate through the program by clicking one of the
elements that visually appear as tabs generally on the upper
portion of the display screen below any tool bars 408. In the
embodiment shown in FIG. 15, the system 1 includes a PROFILES tab
410, a REVIEW tab 412, a METRICS tab 414 and a COACHING tab 620. A
variety of the other tabs with additional information can also be
made available.
[0160] The computer program associated with the present invention
can be utilized to generate a large variety of reports relating to
the recorded call interaction events, the statistical analysis of
each event and the analysis of the event from the application of
the psychological model. The GUI 400 is configured to facilitate a
user's request for a specific reports and to visually display the
Reports on the user's display.
[0161] The REVIEW tab 412 enables the user to locate one or more
caller interaction events (a caller interaction event is also
herein referred to as a "call") stored in the memory. The REVIEW
tab 412 includes visual date fields or links 416, 418 for inputting
a "from" and "to" date range, respectively. Clicking on the links
416, 418 will call a pop-up calendar for selecting a date. A drop
down menu or input field for entering the desired date can also be
used.
[0162] The caller interaction events are divided into folders and
listed by various categories. The folders can be identified or be
sorted by the following event types: upset customer/issue
unresolved; upset customer/issued resolved; program
dissatisfaction; long hold/silence (e.g., caller is placed on hold
for greater than a predetermined time--e.g., 90 seconds--or there
is a period of silence greater than a predetermined amount of
time--e.g., 30 seconds); early hold (i.e., customer is placed on
hold within a predetermined amount of time--e.g., 30 seconds--of
initiating a call); no authentication (i.e., the agent does not
authorize or verify an account within a predetermined time--e.g.,
the first three minutes of the call); inappropriate response (e.g.,
the agent exhibits inappropriate language during the call); absent
agent (i.e., incoming calls where the agent does not answer the
call); long duration for call type (i.e., calls that are a
predetermined percentage over--e.g., 150%--the average duration for
a given call type); and transfers (i.e., calls that end in a
transfer). The categories include: customers, CSR agents, and
customer service events.
[0163] The REVIEW tab 412 includes a visual link to a customers
folder 420. This includes a list of calls subdivided by customer
type. The customer folder 420 may include subfolders for corporate
subsidiaries, specific promotional programs, or event types (i.e.,
upset customer/issue unresolved, etc.).
[0164] The REVIEW tab 412 also includes a visual link to call
center or CSR agent folders 422. This includes a list of calls
divided by call center or CSR agents. The initial breakdown is by
location, followed by a list of managers, and then followed by the
corresponding list of agents. The REVIEW tab 412 also includes a
visual link to a customer service folders 424. This includes a list
of calls subdivided by caller events, call center or CSR agent, and
other relevant events.
[0165] The REVIEW tab 412 also includes a visual SEARCH link 426 to
enable the user to search for calls based on a user-defined
criteria. This include the date range as discussed above.
Additionally, the user can input certain call characteristics or
identifying criteria. For example, the user can input a specific
call ID number and click the SEARCH link 426. This returns only the
desired call regardless of the date of the call. The user could
choose an agent from a drop down menu or list of available agents.
This returns all calls from the selected agent in the date range
specified. The user could also choose a caller (again from a drop
down menu or list of available callers). This returns all calls
from the selected caller(s) within the date range.
[0166] The results from the search are visually depicted as a list
of calls 428 as shown in FIG. 16. Clicking on any call 430 in the
list 428 links the user to a call page 432 (as shown in FIG. 17)
that provides call data and links to an audio file of the call
which can be played on speakers connected to the user's
computer.
[0167] The call page 432 also includes a conversation visual field
434 for displaying a time-based representation of characteristics
of the call based on the psychological behavioral model. The call
page 432 displays a progress bar 436 that illustrates call events
marked with event data shown as, for example, colored points and
colored line segments. A key 440 is provided explaining the
color-coding.
[0168] The call page 432 further includes visual control elements
for playing the recorded call. These include: BACK TO CALL LIST
442; PLAY 444; PAUSE 446; STOP 448; RELOAD 450; REFRESH DATA 452
and START/STOP/DURATION 454. the START/STOP/DURATION 454 reports
the start, stop and duration of distinct call segments occurring in
the call. The distinct call segments occur when there is a
transition from a caller led conversation to an agent led
conversation--or visa versa--and/or the nature of the discussion
shifts to a different topic).
[0169] The REVIEW tab 412 also provides a visual statistics link
456 for displaying call statistics as shown in FIG. 18. The
statistics can include information such as call duration, average
duration for call type, caller talk time, number of holds over
predetermined time periods (e.g., 90 seconds), number of silences,
customer satisfaction score, etc.
[0170] The REVIEW tab 412 also provides a comments link 458. This
will provide a supervisor with the ability to document comments for
each call that can be used in follow-up discussions with the
appropriate agent.
[0171] The METRICS tab 414 allows the user to generate and access
Reports of caller interaction event information. The METRICS tab
414 includes two folders: a standard Reports folder 460 and an
on-demand Reports folder. The standard reports folder 460 includes
pre-defined call performance reports generated by the analytics
engine for daily, weekly, monthly, quarterly, or annual time
intervals. These Reports are organized around two key dimensions:
caller satisfaction and agent performance. The on-demand reports
folder 462 includes pre-defined call performance reports for any
time interval based around two key dimensions: caller and
agent.
[0172] The GUI 400 facilitates generating summary or detailed
Reports as shown in FIG. 19. The user can select a Report time
range via a pop-up calendar. For summary Reports, the user can
select from: client satisfaction; summary by call type; and
non-analyzed calls. For detailed Reports, the user can indicate the
type of Report requested and click the Open Reports link 464.
Additionally, the user can generate Program Reports. The user
selects a client and filters the client by departments or
divisions.
[0173] A CLIENT SATISFACTION REPORT 466 is shown in FIG. 20. The
client satisfaction Report 466 is a summary level report that
identifies analysis results by client for a specified time
interval. The CLIENT SATISFACTION REPORT 466 contains a composite
Satisfaction Score 468 that ranks relative call satisfaction across
event filter criteria. The CLIENT SATISFACTION REPORT 466 is also
available in pre-defined time intervals (for example, daily,
weekly, monthly, quarterly, or annually).
[0174] The CLIENT SATISFACTION REPORT 466 includes a number of
calls column 470 (total number of calls analyzed for the associated
client during the specified reporting interval), an average
duration column 472 (total analyzed talk time for all calls
analyzed for the associated client divided by the total number of
calls analyzed for the client), a greater than (">") 150%
duration column 474 (percentage of calls for a client that exceed
150% of the average duration for all calls per call type), a
greater than 90 second hold column 476 (percentage of calls for a
client where the call center agent places the client on hold for
greater than 90 seconds), a greater than 30 second silence column
478 (percentage of calls for a client where there is a period of
continuous silence within a call greater than 30 seconds), a
customer dissatisfaction column 480 (percentage of calls for a
client where the caller exhibits dissatisfaction or distress--these
calls are in the dissatisfied caller and upset caller/issue
unresolved folders), a program dissatisfaction column 482
(percentage of calls where the caller exhibits dissatisfaction with
the program), and a caller satisfaction column 484 (a composite
score that represents overall caller satisfaction for all calls for
the associated client).
[0175] The caller satisfaction column 484 is defined by a weighted
percentage of the following criteria as shown in FIG. 21: >150%
duration (weight 20%), >90 second hold (10%), >30 second
silence (10%), caller distress (20%), and program dissatisfaction
(20%). All weighted values are subtracted from a starting point of
100.
[0176] The user can generate a summary by CALL TYPE REPORT 486 as
shown in FIG. 22. The CALL TYPE REPORTS 486 identify analysis
results by call type for the specified interval. The summary by
call type Report 486 contains a composite satisfaction score 488
that ranks relative client satisfaction across event filter
criteria. The CALL TYPE REPORT 488 includes a call type column 490,
as well as the other columns described above.
[0177] The user can generate a NON-ANALYZED CALLS REPORT 492 as
shown in FIG. 23. The NON-ANALYZED CALLS REPORT 492 provides a
summary level report that identifies non-analyzed calls for the
specified time interval.
[0178] As shown in FIG. 24, the user can generate a DETAIL LEVEL
REPORT 494. The detail level Report 494 identifies analysis results
by client and call type for the specified time interval. The DETAIL
LEVEL REPORT 494 contain a composite satisfaction score 496 that
ranks relative client satisfaction for each call type across event
filter criteria.
[0179] A PROGRAM REPORT 498 is shown in FIG. 25. This is a detail
level report that identifies analysis results by client departments
or divisions for the specified time interval. THE PROGRAM REPORT
498 contain a composite satisfaction score 500 that ranks relative
client satisfaction for each call type across event filter
criteria.
[0180] The user can also generate a number of CALL CENTER or CSR
AGENT REPORTS. These include the following summary reports:
corporate summary by location; CSR agent performance; and
non-analyzed calls. Additionally, the user can generate team
reports. The team Reports can be broken down by location, team or
agent.
[0181] A CORPORATE SUMMARY BY LOCATION REPORT 502 is shown in FIG.
26. This detail level Report 502 identifies analysis results by
location for the specified time interval, and contains a composite
score that rank relative client performance for each call type
across event filter criteria. The CORPORATE SUMMARY BY LOCATION
REPORT 502 includes a location column 504 (this identifies the call
center location that received the call), a number of calls column
506 (total number of calls received by the associated call center
location during the specified reporting interval, an average
duration column 508 (total analyzed talk time for all calls
analyzed for the associated CSR agent divided by the total number
of calls analyzed for the agent), a greater than 150% duration
column 510 (percentage of calls for a CSR agent that exceed 150% of
the average duration for all calls, a greater than 90 second hold
column 512 (percentage of calls for a CSR agent where the CSR
places the caller on hold for greater than 90 seconds), a greater
than 30 second silence column 514 (percentage of calls for a CSR
agent where there is a period of continuous silence within a call
greater than 30 seconds), a call transfer column 516 (percentage of
calls for a CSR agent that result in the caller being transferred),
an inappropriate response column 518 (percentage of calls where the
CSR agent exhibits inappropriate behavior or language), an
appropriate response column 520 (percentage of calls where the CSR
agent exhibits appropriate behavior or language that result in the
dissipation of caller distress--these calls can be found in the
upset caller/issue resolved folder), a no authentication column 522
(percentage of calls where the CSR agent does not authenticate the
caller's identity to prevent fraud), and a score column 524 (a
composite score that represents overall call center performance for
all calls in the associated call center location.)
[0182] The values 526 in the score column 524 are based on the
weighted criteria shown in FIG. 27. All weighted values are
subtracted from a starting point of 100 except for "appropriate
response," which is an additive value.
[0183] A CSR PERFORMANCE REPORT 528 is shown in FIG. 28. This is a
detail level report that identifies analysis results by CSR for the
specified time interval. This Report 528 contains a composite score
that ranks relative CSR performance for each call type across event
filter criteria.
[0184] FIG. 29 shows a NON-ANALYZED CALLS REPORT 530. This is a
detail level report that identifies analysis results by
non-analyzed CSR calls for a specified time interval.
[0185] A LOCATION BY TEAM REPORT 532 is shown in FIG. 30. This is a
summary level report that identifies analysis results by location
and team for the specified time interval. This Report 532 contains
a composite score that ranks relative CSR performance across event
filter criteria by team.
[0186] FIG. 31 shows a TEAM BY AGENT REPORT 534. This is a summary
level report that identifies analysis results by team and agent for
the specified time interval. These Reports 534 contain a composite
performance score that ranks relative CSR performance across event
filter criteria by agent.
[0187] FIG. 32 shows a CSR BY CALL TYPE REPORT 536. This is detail
level report that identifies analysis results by CSR and call type
for the specified time interval. These Reports 536 contain a
composite performance score that ranks relative CSR performance
across event filter criteria by call type.
[0188] The COACHING tab 620 enables a user to locate one of more
caller interaction events to evaluate and train call center agents
to improve the quality of customer interactions with the agents.
The COACHING tab 620 includes visual date fields 622, 624 for
inputting a "from" and "to date", respectively. Clicking on the
links 416, 418 will call a pop-up calendar for selecting a date. A
drop down menu or input field for entering the desired date can
also be used.
[0189] The COACHING tab 620 displays caller interaction event
information. The caller interaction event information includes
check boxes for selecting the caller interaction event information
as the identifying criteria 626. Based on the selection of the
identifying criteria 626, a plurality of pre-recorded first
communications between outside caller and a specific call center
agent are identified 628. Information relating to the identified
criteria is also displayed 630. A value may be entered in visual
call field 632 to specify the number of pre-identified calls to
display.
[0190] The COACHING tab 620 includes a coaching page 634 to train
the call center agent to improve performance in view of the
identifying criteria, as illustrated in FIG. 35. The coaching page
634 displays a progress bar 636 that illustrates call events marked
with event data shown as, for example, colored points and colored
line segments. The coaching page 634 includes a comment box 640 for
the call agent to indicate areas to be trained. Comments from
others may also be displayed. The coaching page 634 includes a
check-box 638 for requesting additional training on the selected
identifying criteria.
[0191] Referring to FIG. 36, the COACHING tab 620 includes a
graphical representation 642 of the number of calls that are
identified based on the identifying criteria 644. In one
embodiment, the graphical representation displays the percentage of
calls identified based on the identifying criteria 644 for each
week during an identified time period. In this manner, it can be
determined if the training session for the call center agent was
successful.
[0192] While the specific embodiments have been illustrated and
described, numerous modifications come to mind without
significantly departing from the spirit of the invention, and the
scope of protection is only limited by the scope of the
accompanying Claims.
* * * * *